Speed modulated social influence in evacuating pedestrian crowds

Hye Rin Lindsay Lee; Abhishek Bhatia; Jenny Brynjarsdóttir; Nicole Abaid; Alethea Barbaro; Sachit Butail

doi:10.17815/CD.2020.25

Authors

Hye Rin Lindsay Lee Case Western Reserve University, Department of Mathematics, Applied Mathematics and Statistics, Cleveland, USA
Abhishek Bhatia Columbia University, Department of Computer Science, New York, USA and Indraprastha Institute of Information Technology, Delhi, India
Jenny Brynjarsdóttir Case Western Reserve University, Department of Mathematics, Applied Mathematics and Statistics, Cleveland, USA and University of Iceland, Faculty of Physical Sciences, Reykjavík, Iceland
Nicole Abaid Virginia Tech, Department of Mathematics, Blacksburg, USA
Alethea Barbaro Case Western Reserve University, Department of Mathematics, Applied Mathematics and Statistics, Cleveland, USA
Sachit Butail Northern Illinois University, Department of Mechanical Engineering, DeKalb, USA

DOI:

https://doi.org/10.17815/CD.2020.25

Keywords:

evacuation, experiments, models, mixed intentions

Abstract

Evacuation is a complex social phenomenon with individuals tending to exit a confined space as soon as possible. Social factors that influence an individual include collision avoidance and conformity with others with respect to the tendency to exit. While collision avoidance has been heavily focused on by the agent-based models used frequently to simulate evacuation scenarios, these models typically assume that all agents have an equal desire to exit the scene in a given situation. It is more likely that, out of those who are exiting, some are patient while others seek to exit as soon as possible. Here, we experimentally investigate the effect of different proportions of patient (no-rush) versus impatient (rush) individuals in an evacuating crowd of up to 24 people. Our results show that a) average speed changes significantly for individuals who otherwise tended to rush (or not rush) with both type of individuals speeding up in the presence of the other; and b) deviation rate, defined as the amount of turning, changes significantly for the rush individuals in the presence of no-rush individuals. We then seek to replicate this effect with Helbing's social force model with the twin purposes of analyzing how well the model fits experimental data, and explaining the differences in speed in terms of model parameters. We find that we must change the interaction parameters for both rush and no-rush agents depending on the condition that we are modeling in order to fit the model to the experimental data.

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